Evaluation of large language model-generated information in diabetes health patient education: a scoping review
Abstract
Background: Diabetes is a leading cause of disability and death, posing a heavy healthcare burden globally. While standardized health education is crucial for glycemic control and mitigation of complications, traditional educational models face challenges due to insufficient scalability. The ongoing development of AI-based large language model (LLM) methods and technologies presents significant opportunities for health education in the field of diabetes.
Objective: A scoping review of research on LLM-generated information for diabetes patient health education: Synthesizing current application status and performance outcomes.
Methods: The Joanna Briggs Institute (JBI) evidence-based healthcare centre's scoping review guidance was utilized as the methodological framework, then five databases (PubMed, Embase, Web of Science, (American Psychological Association) APA PsycNet, and The Cochrane Library) were searched to retrieve studies from their inception to March 26, 2026. Two reviewers independently performed literature screening, full-text reading, and data extraction.
Results: A total of 21 studies from nine countries were included. Application scenarios were categorized into five domains: general health education, dietary education, complication education, exercise education and technology education. Overall, the existing evidence indicates that LLMs perform well in terms of accuracy and completeness; however, significant limitations remain in readability, reliability, and…
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